
"""simple docstring"""


from __future__ import annotations

import queue



class 	__a	:






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				List[str]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		data
              UpperCamelCase       			=		None
              UpperCamelCase       			=		None





def 						lowerCamelCase__      (				)->				TreeNode:







       """simple docstring"""
       print("\n********Press N to stop entering at any point of time********\n"					)
       UpperCamelCase       			=		input("Enter the value of the root node: "					).strip().lower()
       UpperCamelCase       			=		queue.Queue()
       UpperCamelCase       			=		TreeNode(int(UpperCAmelCase_					)					)
       q.put(UpperCAmelCase_					)
       while not q.empty():
              UpperCamelCase       			=		q.get()
              UpperCamelCase       			=		F"Enter the left node of {node_found.data}: "
              UpperCamelCase       			=		input(UpperCAmelCase_					).strip().lower() or "n"
              if check == "n":
                     return tree_node
              UpperCamelCase       			=		TreeNode(int(UpperCAmelCase_					)					)
              UpperCamelCase       			=		left_node
              q.put(UpperCAmelCase_					)
              UpperCamelCase       			=		F"Enter the right node of {node_found.data}: "
              UpperCamelCase       			=		input(UpperCAmelCase_					).strip().lower() or "n"
              if check == "n":
                     return tree_node
              UpperCamelCase       			=		TreeNode(int(UpperCAmelCase_					)					)
              UpperCamelCase       			=		right_node
              q.put(UpperCAmelCase_					)
       raise



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       print(node.data		,   end=","					)
       pre_order(node.left					)
       pre_order(node.right					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       in_order(node.left					)
       print(node.data		,   end=","					)
       in_order(node.right					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       post_order(node.left					)
       post_order(node.right					)
       print(node.data		,   end=","					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       UpperCamelCase       			=		queue.Queue()
       q.put(UpperCAmelCase_					)
       while not q.empty():
              UpperCamelCase       			=		q.get()
              print(node_dequeued.data		,   end=","					)
              if node_dequeued.left:
                     q.put(node_dequeued.left					)
              if node_dequeued.right:
                     q.put(node_dequeued.right					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       UpperCamelCase       			=		queue.Queue()
       q.put(UpperCAmelCase_					)
       while not q.empty():
              UpperCamelCase       			=		[]
              while not q.empty():
                     UpperCamelCase       			=		q.get()
                     print(node_dequeued.data		,   end=","					)
                     if node_dequeued.left:
                            list_.append(node_dequeued.left					)
                     if node_dequeued.right:
                            list_.append(node_dequeued.right					)
              print()
              for node in list_:
                     q.put(UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       UpperCamelCase       			=		[]
       UpperCamelCase       			=		node
       while n or stack:
              while n:  # start from root node, find its left child
                     print(n.data		,   end=","					)
                     stack.append(UpperCAmelCase_					)
                     UpperCamelCase       			=		n.left
              # end of while means current node doesn't have left child
              UpperCamelCase       			=		stack.pop()
              # start to traverse its right child
              UpperCamelCase       			=		n.right



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       UpperCamelCase       			=		[]
       UpperCamelCase       			=		node
       while n or stack:
              while n:
                     stack.append(UpperCAmelCase_					)
                     UpperCamelCase       			=		n.left
              UpperCamelCase       			=		stack.pop()
              print(n.data		,   end=","					)
              UpperCamelCase       			=		n.right



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				None:







       """simple docstring"""
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					) or not node:
              return
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       UpperCamelCase       			=		node
       stacka.append(UpperCAmelCase_					)
       while stacka:  # to find the reversed order of post order, store it in stack2
              UpperCamelCase       			=		stacka.pop()
              if n.left:
                     stacka.append(n.left					)
              if n.right:
                     stacka.append(n.right					)
              stacka.append(UpperCAmelCase_					)
       while stacka:  # pop up from stack2 will be the post order
              print(stacka.pop().data		,   end=","					)



def 						lowerCamelCase__      (				UpperCAmelCase_ = ""		,   UpperCAmelCase_=50		,   UpperCAmelCase_="*"					)->				str:







       """simple docstring"""
       if not s:
              return "\n" + width * char
       UpperCamelCase			,							UpperCamelCase       			=		divmod(width - len(UpperCAmelCase_					) - 2		,   2					)
       return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(prompt("""Binary Tree Traversals"""))

      SCREAMING_SNAKE_CASE			  =  build_tree()
      print(prompt("""Pre Order Traversal"""))
      pre_order(node)
      print(prompt() + """\n""")

      print(prompt("""In Order Traversal"""))
      in_order(node)
      print(prompt() + """\n""")

      print(prompt("""Post Order Traversal"""))
      post_order(node)
      print(prompt() + """\n""")

      print(prompt("""Level Order Traversal"""))
      level_order(node)
      print(prompt() + """\n""")

      print(prompt("""Actual Level Order Traversal"""))
      level_order_actual(node)
      print("""*""" * 50 + """\n""")

      print(prompt("""Pre Order Traversal - Iteration Version"""))
      pre_order_iter(node)
      print(prompt() + """\n""")

      print(prompt("""In Order Traversal - Iteration Version"""))
      in_order_iter(node)
      print(prompt() + """\n""")

      print(prompt("""Post Order Traversal - Iteration Version"""))
      post_order_iter(node)
      print(prompt())







"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				Any=400							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Dict=0.9							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=[0.5, 0.5, 0.5]							,   UpperCAmelCase_			:				Union[str, Any]=[0.5, 0.5, 0.5]							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		size if size is not None else {"shortest_edge": 30}
              UpperCamelCase       			=		crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize_and_center_crop
              UpperCamelCase       			=		size
              UpperCamelCase       			=		crop_pct
              UpperCamelCase       			=		crop_size
              UpperCamelCase       			=		do_normalize
              UpperCamelCase       			=		image_mean
              UpperCamelCase       			=		image_std






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       List[Any]				=      PoolFormerImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		PoolFormerImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize_and_center_crop"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "crop_pct"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_normalize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_mean"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_std"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 30}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 30, "width": 30}      )

              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 42}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values

              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )





